A systems genomics approach to uncover the molecular properties of cancer genes

Abstract Genes involved in cancer are under constant evolutionary pressure, potentially resulting in diverse molecular properties. In this study, we explore 23 omic features from publicly available databases to define the molecular profile of different classes of cancer genes. Cancer genes were grou...

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Autores principales:	Felix Grassmann, Yudi Pawitan, Kamila Czene
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f63a855622624344994a11987a33535c
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spelling	oai:doaj.org-article:f63a855622624344994a11987a33535c2021-12-02T15:09:22ZA systems genomics approach to uncover the molecular properties of cancer genes10.1038/s41598-020-75400-22045-2322https://doaj.org/article/f63a855622624344994a11987a33535c2020-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-75400-2https://doaj.org/toc/2045-2322Abstract Genes involved in cancer are under constant evolutionary pressure, potentially resulting in diverse molecular properties. In this study, we explore 23 omic features from publicly available databases to define the molecular profile of different classes of cancer genes. Cancer genes were grouped according to mutational landscape (germline and somatically mutated genes), role in cancer initiation (cancer driver genes) or cancer survival (survival genes), as well as being implicated by genome-wide association studies (GWAS genes). For each gene, we also computed feature scores based on all omic features, effectively summarizing how closely a gene resembles cancer genes of the respective class. In general, cancer genes are longer, have a lower GC content, have more isoforms with shorter exons, are expressed in more tissues and have more transcription factor binding sites than non-cancer genes. We found that germline genes more closely resemble single tissue GWAS genes while somatic genes are more similar to pleiotropic cancer GWAS genes. As a proof-of-principle, we utilized aggregated feature scores to prioritize genes in breast cancer GWAS loci and found that top ranking genes were enriched in cancer related pathways. In conclusion, we have identified multiple omic features associated with different classes of cancer genes, which can assist prioritization of genes in cancer gene discovery.Felix GrassmannYudi PawitanKamila CzeneNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Felix Grassmann Yudi Pawitan Kamila Czene A systems genomics approach to uncover the molecular properties of cancer genes
description	Abstract Genes involved in cancer are under constant evolutionary pressure, potentially resulting in diverse molecular properties. In this study, we explore 23 omic features from publicly available databases to define the molecular profile of different classes of cancer genes. Cancer genes were grouped according to mutational landscape (germline and somatically mutated genes), role in cancer initiation (cancer driver genes) or cancer survival (survival genes), as well as being implicated by genome-wide association studies (GWAS genes). For each gene, we also computed feature scores based on all omic features, effectively summarizing how closely a gene resembles cancer genes of the respective class. In general, cancer genes are longer, have a lower GC content, have more isoforms with shorter exons, are expressed in more tissues and have more transcription factor binding sites than non-cancer genes. We found that germline genes more closely resemble single tissue GWAS genes while somatic genes are more similar to pleiotropic cancer GWAS genes. As a proof-of-principle, we utilized aggregated feature scores to prioritize genes in breast cancer GWAS loci and found that top ranking genes were enriched in cancer related pathways. In conclusion, we have identified multiple omic features associated with different classes of cancer genes, which can assist prioritization of genes in cancer gene discovery.
format	article
author	Felix Grassmann Yudi Pawitan Kamila Czene
author_facet	Felix Grassmann Yudi Pawitan Kamila Czene
author_sort	Felix Grassmann
title	A systems genomics approach to uncover the molecular properties of cancer genes
title_short	A systems genomics approach to uncover the molecular properties of cancer genes
title_full	A systems genomics approach to uncover the molecular properties of cancer genes
title_fullStr	A systems genomics approach to uncover the molecular properties of cancer genes
title_full_unstemmed	A systems genomics approach to uncover the molecular properties of cancer genes
title_sort	systems genomics approach to uncover the molecular properties of cancer genes
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/f63a855622624344994a11987a33535c
work_keys_str_mv	AT felixgrassmann asystemsgenomicsapproachtouncoverthemolecularpropertiesofcancergenes AT yudipawitan asystemsgenomicsapproachtouncoverthemolecularpropertiesofcancergenes AT kamilaczene asystemsgenomicsapproachtouncoverthemolecularpropertiesofcancergenes AT felixgrassmann systemsgenomicsapproachtouncoverthemolecularpropertiesofcancergenes AT yudipawitan systemsgenomicsapproachtouncoverthemolecularpropertiesofcancergenes AT kamilaczene systemsgenomicsapproachtouncoverthemolecularpropertiesofcancergenes
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A systems genomics approach to uncover the molecular properties of cancer genes

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